EP3564693B1 - Verfahren und vorrichtung zur berechnung eines ladezustandes einer batterie in echtzeit - Google Patents

Verfahren und vorrichtung zur berechnung eines ladezustandes einer batterie in echtzeit Download PDF

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EP3564693B1
EP3564693B1 EP18785067.2A EP18785067A EP3564693B1 EP 3564693 B1 EP3564693 B1 EP 3564693B1 EP 18785067 A EP18785067 A EP 18785067A EP 3564693 B1 EP3564693 B1 EP 3564693B1
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Prior art keywords
error
soc
noise variable
battery
noise
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EP3564693A1 (de
EP3564693A4 (de
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Bo Mi Lim
Won Tae Joe
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LG Energy Solution Ltd
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LG Energy Solution Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/3644—Constructional arrangements
    • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health

Definitions

  • the present invention relates to a device and method for calculating a State Of Charge (SOC) of a battery by reflecting noise.
  • SOC State Of Charge
  • the present invention relates to a device and method for calculating an SOC of a battery in consideration of noise generated in measuring a value used for battery SOC calculation.
  • a high output secondary battery using a non-aqueous electrolytic solution with a high energy density has been developed, and a plurality of high-output secondary batteries are connected in series to constitute a large-capacity secondary battery so that it may be used for driving a motor, such as an electric vehicle, which requires a large electric power.
  • one large-capacity secondary battery (hereinafter, referred to as a battery module for convenience of explanation throughout the specification) is usually composed of a plurality of secondary batteries connected in series (hereinafter referred to as a unit battery for convenience of explanation throughout the specification).
  • the battery module particularly, the HEV secondary battery module
  • the HEV secondary battery module as several to dozens of unit cells alternate between charge and discharge, there is a need to control such charge/discharge and the like so as to maintain the battery module in an appropriate operating state.
  • the HEV battery module detects the SOC of a battery through calculation by detecting voltage, current, temperature, etc. of the battery, and perform the SOC control so that the fuel consumption efficiency of the vehicle becomes the best.
  • the SOC level at this time is generally set so that, for example, the SOC is in the range of 50% to 70%.
  • the SOC may be controlled to charge and discharge over the entire range of the energy storage system from 0% to 100%.
  • an SOC has been calculated based on an external error value arbitrarily set at the beginning.
  • the present invention provides a device and method for accurately calculating an SOC of a battery by setting an external error value suitable for noise of various environments.
  • a method of calculating a State Of Charge (SOC) of a battery in real time includes: a battery parameter measurement operation for measuring a current, a voltage, a temperature, a degradation degree of the battery; and a real-time SOC calculation operation for calculating an SOC of the battery in real time based on the measured battery parameter; wherein the real-time SOC calculation operation comprises a real-time SOC arithmetic operation for calculating an SOC of a battery in real time using Extended Kalman Filter below.
  • the real-time SOC calculation operation further includes a noise variable setting operation for setting a noise variable W set by an external error in the Extended Kalman Filter.
  • the noise variable setting operation includes: an external error setting operation for setting an error occurring when each battery parameter is measured; a temporary noise variable setting operation for setting a temporary noise variable to be used as a noise variable of the Extended Kalman Filter; and an error calculation operation for calculating an error between an SOC calculated by reflecting the external error and the temporary noise variable and an SOC calculated without reflecting the external error.
  • the noise variable setting operation repeatedly performs the error calculation operation by changing the external error and the temporary noise to various values.
  • the noise variable setting operation further includes a final noise variable detection operation for detecting a final noise variable optimized for the external error, wherein the final noise variable detection operation may set a temporary noise variable in which an error calculated in the error calculation operation falls within a predetermined range as a final noise variable.
  • a real time battery State Of Charge (SOC) calculation device includes: a parameter measurement unit configured to measure a current, a voltage, a temperature, and a degradation degree of a battery; and a real-time SOC calculation unit configured to receive the current, voltage, temperature, and degradation degree of the battery measured in the parameter measurement unit to calculate an SOC of the battery in real time, wherein the real-time SOC calculation unit comprises an operation unit for calculating the SOC of the battery in real time using Extended Kalman Filter below.
  • SOC State Of Charge
  • the real-time SOC calculation unit further includes a noise variable setting unit for setting a noise variable W by an external error in the Kalman expansion filter and inputting the noise variable W to the operation unit.
  • the noise variable setting unit includes: an external error setting module configured to receive an error occurring when each battery parameter is measured from a user and set an external error; a temporary noise variable setting module configured to receive a temporary noise variable to be used as a noise variable of the Kalman expansion filter from a user and set a temporary noise variable; and an error calculation module configured to calculate an error between an SOC calculated based on the external error and the temporary noise variable and an SOC without the external error.
  • the noise variable setting unit further includes a final noise detection module for detecting a final noise variable optimized for the external error, wherein the final noise detection module may detect a temporary noise variable in which the error calculated in the error calculation module falls within a predetermined range as a final noise variable.
  • the present invention may calculate an accurate SOC of a battery by setting an external error value suitable for noise in various external environments.
  • first may be referred to as a second component and vice versa without departing from the scope of the present invention.
  • Terms used in this specification are used to describe specific embodiments, and are not intended to limit the scope of the present invention.
  • the singular expressions include plural expressions unless the context clearly dictates otherwise.
  • the remaining capacity of a battery may mean a charge state of the battery, that is, a State Of Charge (SOC).
  • SOC State Of Charge
  • FIG. 1 is a flowchart of a method of calculating a battery SOC in real time according to an embodiment of the present invention.
  • a method of calculating the SOC of a battery in real time may include a battery parameter measurement operation S100 for measuring current, voltage, temperature, and degradation degree of battery, and a real-time SOC calculation operation S200 for calculating the SOC of the battery in real time based on the measured battery parameter.
  • the battery parameter measurement operation S100 is an operation for measuring current battery state values, which are measured in a battery management system (BMS) mounted on the battery.
  • Current battery status values such as battery current, voltage, temperature, degradation, etc., may be measured by a separate device.
  • the real-time SOC calculation operation S200 may include a real-time SOC arithmetic operation S220 for calculating the SOC of the battery in real time using an Extended Kalman Filter.
  • the Extended Kalman Filter may be based on Equation 1 below.
  • the Extended Kalman Filter may be roughly divided into a prediction operation and a correction operation.
  • the prediction operation is an operation for calculating a predicted state when a user input is received according to a state estimated at a previous time
  • the correction operation is an operation for calculating an accurate state based on the predicted state and the actual measurement state.
  • the Extended Kalman Filter may be a method of reducing the error by repeatedly performing prediction -> correction -> prediction -> correction.
  • an equation for calculating an SOC, f (x, u, 0), may be calculated based on Equation 2 below.
  • SOC k + 1 V 1 , k + 1 1 0 0 exp ⁇ ⁇ t R 1 C 1 SOC k V 1 , k + ⁇ t Capacity R 1 1 ⁇ exp ⁇ ⁇ t R 1 C 1 I k
  • the real-time SOC calculation operation S200 may further include a noise variable setting operation S210 for setting a noise variable W set by a noise value of an external process.
  • the noise variable setting operation S210 may include an external error setting operation S211 for setting an external error inevitably occurring when measuring each battery parameter, a temporary noise variable setting operation S212 for setting a temporary noise variable to be used as a noise variable W of the Extended Kalman Filter, and an error calculation operation S213 for calculating an error between the SOC calculated based on the external error and the temporary noise variable and the SOC having no external error.
  • the noise variable setting operation S210 may repeatedly perform the error calculation operation S213 by changing the external error and the temporary noise variable to various values.
  • the error calculation operation S213 may express the error value as a time-error graph.
  • FIG. 2 is an example of a time-error graph showing an error of a noise variable of a voltage model in an error calculation operation.
  • the smallest value of the noise variable of the voltage model may be advantageous.
  • the noise variable setting operation S210 may further include a final noise variable detection operation S214 for detecting a final noise variable optimized for the external error.
  • the final noise variable may be a temporary noise variable whose error calculated in the error calculation operation S213 falls within a predetermined range.
  • FIGS. 3 to 6 are error graphs for the noise variables of each model of voltage, temperature, current, and degradation.
  • FIG. 3 is an error graph for the noise variable of a voltage model when there is a voltage error.
  • the error may differ by up to 2%.
  • the error according to the noise variable of the current model is maximum 1%. If there is an error in the external voltage, in relation to the final noise variable, it may be the right method to set the weight for the noise variable of the voltage model to be as low as possible and to set the weight for the noise variable of the current model to be high.
  • FIG. 4 is an error graph for the noise variable of a voltage model when there is a temperature error.
  • FIG. 5 is an error graph for the noise variable of a voltage model when there is a current error.
  • the SOC error change rate for the noise variable of the current model is 9% or more, compared with the maximum SOC error change rate of 2%.
  • the noise variable of the voltage model may be set to a small value, the weight corresponding to the voltage model may be set to be large, and the weight corresponding to the current model may be set to be low.
  • FIG. 6 is a graph illustrating an error occurring according to a degradation degree and a weight corresponding to a current model and a voltage model.
  • the upper graph of FIG. 6 is an error graph showing an error occurring according to a weight corresponding to the current model and the voltage model when the degradation degree is 5%.
  • the graph has a similar value regardless of the weight of the current model and the weight of the voltage model.
  • the weight corresponding to the voltage model may be set to be higher as the degradation degree progresses.
  • FIG. 7 is a graph showing an error of a voltage model and a current model according to the presence or absence of the external error.
  • the noise variable of the voltage model may be set to be as small as possible and the weight corresponding to the voltage model may be set to be large.
  • FIG. 8 is a general configuration diagram of a real-time battery SOC calculation device according to an embodiment of the present invention.
  • a real-time battery SOC calculation device includes a parameter measurement unit 100 for measuring the current, voltage, temperature and degradation degree of the battery, and a real time SOC calculation unit 200 for calculating the SOC of the battery in real time based on the measured current, voltage, temperature and degradation degree of the battery measured in the parameter measurement unit 100.
  • parameter measurement unit 100 has been described as a separate configuration in the present invention, it may be measured in a battery management system that is basically installed in the battery.
  • the real-time SOC calculation unit 200 may include an operation unit 210 for calculating the SOC of the battery in real time using the Extended Kalman Filter.
  • the Extended Kalman Filter is the same as the Extended Kalman Filter used in the method of calculating the SOC of the battery in real time according to an embodiment of the present invention, and a detailed description thereof will be omitted.
  • the real-time SOC calculation unit 200 may further include a noise variable setting unit 220 for setting a noise variable according to an external error to input the noise variable to the operation unit.
  • the noise variable setting unit 220 includes an external error setting module 221 for receiving an error occurring when measuring each battery parameter from a user and setting an external error, a temporary noise variable setting module 222 for receiving a temporary noise variable to be used as a noise variable of the Kalman expansion filter from a user and setting a temporary noise variable, and an error calculation module 223 for calculating the SOC calculated based on the external error and the temporary noise variable and the SOC having no external error.
  • the external error setting module 221 may perform an external error setting operation of the battery SOC calculating method in real time according to the embodiment of the present invention.
  • the temporary noise variable setting module 222 may perform a temporary noise variable setting operation of the battery SOC calculation method in real time according to the embodiment of the present invention.
  • the error calculation module 223 may perform an error calculation operation of the battery SOC calculation method in real time according to the embodiment of the present invention.
  • the noise variable setting unit 220 may further include a final noise variable detection module 224 for detecting a final noise variable optimized for the external error.
  • the final noise variable detection module 224 may detect a temporary noise variable, in which the error calculated in the error calculation module 223 falls within a predetermined range, as a final noise variable.
  • the operation unit 210 may calculate a real-time SOC by substituting the value set in the noise variable setting unit 220 into the Kalman filter of Equation 1.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Secondary Cells (AREA)
  • Tests Of Electric Status Of Batteries (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Claims (2)

  1. Verfahren zum Berechnen eines Ladungszustands (SOC) einer Batterie in Echtzeit, das Verfahren umfassend:
    einen Batterieparameter-Messvorgang (S100) zum Messen eines Stroms, einer Spannung, einer Temperatur, eines Verschlechterungsgrads der Batterie; und
    einen Echtzeit-SOC-Berechnungsvorgang (S200) zum Berechnen eines SOC der Batterie in Echtzeit auf Grundlage des gemessenen Batterieparameters;
    wobei der Echtzeit-SOC-Berechnungsvorgang (S200) einen Echtzeit-SOC-Arithmetikvorgang (S220) zum Berechnen eines SOC einer Batterie in Echtzeit unter Verwendung des untenstehenden Extended Kalmanfilter umfasst:
    (Extended Kalmanfilter)
    Figure imgb0007
    (P: Fehlerkovarianz, H: Transformationskoeffizient, K: Kalman-Verstärkung, Q: Standardabweichung des wahren Werts, W: Systemrauschen, x: Zustandsvariable(SOC), z: Beobachtungswert, V: Fehler bezüglich wahrem Beobachtungswert, k: Betriebsgrad, u: zusätzlicher Eingabewert, f(x,u,o): Gleichung zum Berechnen eines SOC, I: Einheitsmatrix),
    wobei der Echtzeit-SOC-Berechnungsvorgang (S200) ferner einen Rauschvariable-Setzvorgang (S210) zum Setzen einer Rauschvariablen W umfasst, welche durch einen externen Fehler in dem Extended Kalmanfilter gesetzt wird, und
    wobei der Rauschvariable-Setzvorgang (S210) umfasst:
    einen externen Fehler-Setzvorgang (S211) zum Setzen eines Fehlers, welcher auftritt, wenn jeder Batterieparameter gemessen wird;
    einen temporären Rauschvariable-Setzvorgang (S212) zum Setzen einer temporären Rauschvariablen, welche als eine Rauschvariable des Extended Kalmanfilter zu verwenden ist;
    einen Fehler-Berechnungsvorgang (S213) zum Berechnen eines Fehlers zwischen einem SOC, welcher durch Wiedergeben des externen Fehlers und der temporären Rauschvariablen berechnet wird, und einem SOC, welcher ohne Wiedergeben des externen Fehlers berechnet wird, und
    einen finalen Rauschvariable-Detektionsvorgang (S214) zum Detektieren einer finalen Rauschvariablen, welche für den externen Fehler optimiert ist;
    wobei der Rauschvariable-Setzvorgang (S210) wiederholt den Fehler-Berechnungsvorgang (S213) durchführt, indem der externe Fehler und das temporäre Rauschen auf verschiedene Werte geändert werden;
    wobei der finale Rauschvariable-Detektionsvorgang (s214) eine temporäre Rauschvariable, in welcher ein in dem Fehler-Berechnungsvorgang (S213) berechneter Fehler innerhalb eines vorbestimmten Bereichs fällt, als eine finale Rauschvariable setzt; und
    wobei die Rauschvariable des Spannungsmodells dazu gesetzt wird, so klein wie möglich zu sein, und das dem Spannungsmodell entsprechende Gewicht dazu gesetzt wird, groß zu sein.
  2. Echtzeit-Batterie-Ladungszustand (SOC)-Berechnungsvorrichtung, umfassend:
    eine Parameter-Messeinheit (100), welche dazu eingerichtet ist, einen Strom, eine Spannung, eine Temperatur und einen Verschlechterungsgrad einer Batterie zu messen; und
    eine Echtzeit-SOC-Berechnungseinheit (200), welche dazu eingerichtet ist, den Strom, die Spannung, die Temperatur und den Verschlechterungsgrad der Batterie zu erhalten, welche in der Parameter-Messeinheit (100) gemessen werden, um einen SOC der Batterie in Echtzeit zu berechnen,
    wobei die Echtzeit-SOC-Berechnungseinheit (200) eine Betriebseinheit (210) zum Berechnen des SOC der Batterie in Echtzeit unter Verwendung des untenstehenden Extended Kalmanfilters umfasst
    (Extended Kalmanfilter)
    Figure imgb0008
    (P: Fehlerkovarianz, H: Transformationskoeffizient, K: Kalman-Verstärkung, Q: Standardabweichung des wahren Werts, W: Systemrauschen, x: Zustandsvariable(SOC), z: Beobachtungswert, V: Fehler bezüglich wahrem Beobachtungswert, k: Betriebsgrad, u: zusätzlicher Eingabewert, f(x,u,o): Gleichung zum Berechnen eines SOC, I: Einheitsmatrix),
    wobei die Echtzeit-SOC-Berechnungseinheit (200) ferner eine Rauschvariable-Setzeinheit (220) zum Setzen einer Rauschvariablen W durch einen externen Fehler in dem Kalman-Expansionsfilter und Eingeben der Rauschvariablen W zu der Betriebseinheit (210) umfasst,
    wobei die Rauschvariable-Setzeinheit (220) umfasst:
    ein externes Fehler-Setzmodul (221), welches dazu eingerichtet ist, einen Fehler zu erhalten, welcher auftritt, wenn jeder Batterieparameter von einem Benutzer gemessen wird und ein externer Fehler gesetzt wird;
    ein temporäres Rauschvariable-Setzmodul (222), welches dazu eingerichtet ist, eine temporäre Rauschvariable, welche als eine Rauschvariable des Kalman-Expansionsfilters zu verwenden ist, von einem Benutzer zu erhalten und eine temporäre Rauschvariable zu setzen; und
    ein Fehler-Berechnungsmodul (223), welches dazu eingerichtet ist, einen Fehler zwischen einem SOC, welcher auf Grundlage des externen Fehlers und der temporären Rauschvariablen berechnet wird, und einem SOC ohne den externen Fehler zu berechnen,
    ein finales Rausch-Detektionsmodul (224) zum Detektieren einer finalen Rauschvariablen, welche für den externen Fehler optimiert und dazu eingerichtet ist, eine temporäre Rauschvariable, in welcher der in dem Fehler-Berechnungsmodul (223) berechnete Fehler innerhalb eines vorbestimmten Bereichs fällt, als eine finale Rauschvariable zu detektieren;
    wobei die Rauschvariable des Spannungsmodells dazu gesetzt wird, so klein wie möglich zu sein, und das Gewicht, welches dem Spannungsmodell entspricht, dazu gesetzt wird, groß zu sein.
EP18785067.2A 2017-04-12 2018-02-23 Verfahren und vorrichtung zur berechnung eines ladezustandes einer batterie in echtzeit Active EP3564693B1 (de)

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US20190346511A1 (en) 2019-11-14
EP3564693A1 (de) 2019-11-06
PL3564693T3 (pl) 2023-10-09
WO2018190508A1 (ko) 2018-10-18
EP3564693A4 (de) 2020-01-22
US11340298B2 (en) 2022-05-24

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